AI Research Addresses One-Sided Conversation Problem in Real-World Applications

Researchers have defined the one-sided conversation problem (1SC), which focuses on inferring and learning from a single side of a dialogue, a limitation found in contexts such as telemedicine, call centers, and smart glasses. The investigation targets two main tasks: filling in absent speaker turns for real-time use and creating summaries from one-sided transcripts. Evaluation utilized prompting and finetuned models across datasets like MultiWOZ, DailyDialog, and Candor, with assessments via human A/B testing and LLM-as-a-judge metrics. Results show that having access to one future turn and utterance length information improves reconstruction, while placeholder prompting minimizes hallucination. Large models yield promising results with prompting, while smaller models need finetuning. The study also reveals that high-quality summaries can be produced without reconstructing missing turns, providing viable solutions for AI in limited environments. This research is detailed in arXiv:2511.03056v2, marking progress in conversational AI for situations where complete dialogue recording is impractical.